The Effect of Park and Urban Environments on Coronary Artery Disease Patients: A Randomized Trial

© 2015 Regina Grazuleviciene et al. Aim. To test the hypothesis that walking in a park has a greater positive effect on coronary artery disease (CAD) patients' hemodynamic parameters than walking in an urban environment. Methods. Twenty stable CAD patients were randomized into two groups: 30-minute walk on 7 consecutive days in either a city park or busy urban street. Wilcoxon signed-rank test was employed to study short-term (30 min) and cumulative changes (following 7 consecutive days of exposure) in resting hemodynamic parameters in different environments. Results. There were no statistically significant differences in the baseline and peak exercise systolic blood pressure (SBP), diastolic blood pressure (DBP), heart rate (HR), exercise duration, or HR recovery in urban versus park exposure groups. Seven days of walking slightly improved all hemodynamic parameters in both groups. Compared to baseline, the city park group exhibited statistically significantly greater reductions in HR and DBP and increases in exercise duration and HR recovery. The SBP and DBP changes in the urban exposed group were lower than in the park exposed group. Conclusions. Walking in a park had a greater positive effect on CAD patients' cardiac function than walking in an urban environment, suggesting that rehabilitation through walking in green environments after coronary events should be encouraged

Exposure to natural environments during pregnancy and birth outcomes in 11 european birth cohorts

Research suggests that maternal exposure to natural environments (i.e., green and blue spaces) promotes healthy fetal growth. However, the available evidence is heterogeneous across regions, with very few studies on the effects of blue spaces. This study evaluated associations between maternal exposure to natural environments and birth outcomes in 11 birth cohorts across nine European countries. This study, part of the LifeCycle project, was based on a total sample size of 69,683 newborns with harmonised data. For each participant, we calculated seven indicators of residential exposure to natural environments: surrounding greenspace in 100m, 300m, and 500m using Normalised Difference Vegetation Index (NDVI) buffers, distance to the nearest green space, accessibility to green space, distance to the nearest blue space, and accessibility to blue space. Measures of birth weight and small for gestational age (SGA) were extracted from hospital records. We used pooled linear and logistic regression models to estimate associations between exposure to the natural environment and birth outcomes, controlling for the relevant covariates. We evaluated the potential effect modification by socioeconomic status (SES) and region of Europe and the influence of ambient air pollution on the associations. In the pooled analyses, residential surrounding greenspace in 100m, 300m, and 500m buffer was associated with increased birth weight and lower odds for SGA. Higher residential distance to green space was associated with lower birth weight and higher odds for SGA. We observed close to null associations for accessibility to green space and exposure to blue space. We found stronger estimated magnitudes for those participants with lower educational levels, from more deprived areas, and living in the northern European region. Our associations did not change notably after adjustment for air pollution. These findings may support implementing policies to promote natural environments in our cities, starting in more deprived areas. © 2022Funding text 1: This project received funding from the European Union's Horizon 2020 research and innovation programme (LIFECYCLE, grant agreement No 733206; EUCAN-Connect grant agreement No 824989). ISGlobal acknowledges support from the Spanish Ministry of Science and Innovation and State Research Agency through the “Centro de Excelencia Severo Ochoa 2019-2023” Program (CEX2018-000806-S), and support from the Generalitat de Catalunya through the CERCA Program. For more information of each cohort individual funding, see Supplementary Material s, Information S2. ; Funding text 2: We would like to thanks to all the mothers, fathers, and children for their generous contribution as participants in the cohorts that are part of the LifeCycle project. For more information of each cohort individual acknowledgment, see Supplementary Materials, Information S1. This project received funding from the European Union's Horizon 2020 research and innovation programme (LIFECYCLE, grant agreement No 733206; EUCAN-Connect grant agreement No 824989). ISGlobal acknowledges support from the Spanish Ministry of Science and Innovation and State Research Agency through the “Centro de Excelencia Severo Ochoa 2019-2023” Program (CEX2018-000806-S), and support from the Generalitat de Catalunya through the CERCA Program. For more information of each cohort individual funding, see Supplementary Materials, Information S2. DAL has received support from Medtronic Ltd and Roche Diagnostics for research unrelated to this study. All the other authors declare that they have no competing interests

Urban environment and health behaviours in children from six European countries

Background: Urban environmental design is increasingly considered influential for health and wellbeing, but evidence is mostly based on adults and single exposure studies. We evaluated the association between a wide range of urban environment characteristics and health behaviours in childhood. Methods: We estimated exposure to 32 urban environment characteristics (related to the built environment, traffic, and natural spaces) for home and school addresses of 1,581 children aged 6-11 years from six European cohorts. We collected information on health behaviours including total amount of overall moderate-to-vigorous physical activity, physical activity outside school hours, active transport, sedentary behaviours and sleep duration, and developed patterns of behaviours with principal component analysis. We used an exposure-wide association study to screen all exposure-outcome associations, and the deletion-substitution-addition algorithm to build a final multi-exposure model. Results: In multi-exposure models, green spaces (Normalized Difference Vegetation Index, NDVI) were positively associated with active transport, and inversely associated with sedentary time (22.71 min/day less (95%CI -39.90, -5.51) per interquartile range increase in NDVI). Residence in densely built areas was associated with more physical activity and less sedentary time, and densely populated areas with less physical activity outside school hours and more sedentary time. Presence of a major road was associated with lower sleep duration (-4.80 min/day (95%CI -9.11, -0.48); compared with no major road). Results for the behavioural patterns were similar. Conclusions: This multicohort study suggests that areas with more vegetation, more building density, less population density and without major roads are associated with improved health behaviours in childhood

In-utero and childhood chemical exposome in six European mother-child cohorts

Background: Harmonized data describing simultaneous exposure to a large number of environmental contaminants in-utero and during childhood is currently very limited. Objectives: To characterize concentrations of a large number of environmental contaminants in pregnant women from Europe and their children, based on chemical analysis of biological samples from mother-child pairs. Methods: We relied on the Early-Life Exposome project, HELIX, a collaborative project across six established population-based birth cohort studies in Europe. In 1301 subjects, biomarkers of exposure to 45 contaminants (i.e. organochlorine compounds, polybrominated diphenyl ethers, per- and polyfluoroalkyl substances, toxic and essential elements, phthalate metabolites, environmental phenols, organophosphate pesticide metabolites and cotinine) were measured in biological samples from children (6-12 years) and their mothers during pregnancy, using highly sensitive biomonitoring methods. Results: Most of the exposure biomarkers had high detection frequencies in mothers (35 out of 45 biomarkers with >90% detected) and children (33 out of 45 biomarkers with >90% detected). Concentrations were significantly different between cohorts for all compounds, and were generally higher in maternal compared to children samples. For most of the persistent compounds the correlations between maternal and child concentrations were moderate to high (Spearman Rho > 0.35), while for most non-persistent compounds correlations were considerably lower (Spearman Rho 100,000 concentrations of environmental contaminants in mother-child pairs forms a unique possibility for conducting epidemiological studies using an exposome approach.The research leading to these results has received funding from the European Community's Seventh Framework Programme (FP7/2007–2013) under grant agreement no 308333 – the HELIX project. Dr. Regina Grazuleviciene received the grant of the Lithuanian Agency for Science Innovation and Technology (No. 31V-77). Dr. Maribel Casas received funding from Instituto de Salud Carlos III (Ministry of Economy and Competitiveness) (MS16/00128). Rosie McEachan and John Wright were supported by the NIHR CLAHRC Yorkshire and Humber (IS-CLA-0113-10020). www.clahrc-yh.nihr.ac.uk. The INMA (Environment and Childhood) Sabadell cohort and biomarker measurements were funded by grants from Instituto de Salud Carlos III (Red INMA G03/176; CB06/02/0041; PI041436; PI081151 incl. FEDER funds; PI12/01890 incl. FEDER funds; CP13/00054 incl. FEDER funds), CIBERESP, Generalitat de Catalunya-CIRIT 1999SGR 00241, Generalitat de Catalunya-AGAUR (2009 SGR 501, 2014 SGR 822), Fundació La marató de TV3 (090430), Spanish Ministry of Economy and Competitiveness (SAF2012-32991 incl. FEDER funds). The Norwegian Mother and Child Cohort Study is supported by the Norwegian Ministry of Health and Care Services and the Ministry of Education and Research, NIH/NINDS (grant no.1 UO1 NS 047537-01 and grant no.2 UO1 NS 047537-06A1). The REAH cohort was financially supported by European projects (EU FP6-2003-Food-3-NewGeneris, EU FP6. STREP Hiwate, EU FP7 ENV.2007.1.2.2.2. Project No 211250 Escape, EU FP7-2008-ENV-1.2.1.4 Envirogenomarkers, EU FP7-HEALTH-2009- single stage CHICOS, EU FP7 ENV.2008.1.2.1.6. Proposal No 226285 ENRIECO, EU- FP7- HEALTH-2012 Proposal No 308333 HELIX), the Greek Ministry of Health (Program of Prevention of obesity and neurodevelopmental disorders in preschool children, in Heraklion district, Crete, Greece: 2011–2014; “Rhea Plus”: Primary Prevention Program of Environmental Risk Factors for Reproductive Health, and Child Health: 2012–15)

Development of West-European PM_2.5 and NO₂ land use regression models incorporating satellite-derived and chemical transport modelling data.

Satellite-derived (SAT) and chemical transport model (CTM) estimates of PM2.5 and NO2 are increasingly used in combination with Land Use Regression (LUR) models. We aimed to compare the contribution of SAT and CTM data to the performance of LUR PM2.5 and NO2 models for Europe. Four sets of models, all including local traffic and land use variables, were compared (LUR without SAT or CTM, with SAT only, with CTM only, and with both SAT and CTM). LUR models were developed using two monitoring data sets: PM2.5 and NO2 ground level measurements from the European Study of Cohorts for Air Pollution Effects (ESCAPE) and from the European AIRBASE network. LUR PM2.5 models including SAT and SAT+CTM explained ~60% of spatial variation in measured PM2.5 concentrations, substantially more than the LUR model without SAT and CTM (adjR(2): 0.33-0.38). For NO2 CTM improved prediction modestly (adjR(2): 0.58) compared to models without SAT and CTM (adjR(2): 0.47-0.51). Both monitoring networks are capable of producing models explaining the spatial variance over a large study area. SAT and CTM estimates of PM2.5 and NO2 significantly improved the performance of high spatial resolution LUR models at the European scale for use in large epidemiological studies

Early-life environmental exposures and blood pressure in children

Background: Growing evidence exists about the fetal and environmental origins of hypertension, but mainly limited to single-exposure studies. The exposome has been proposed as a more holistic approach by studying many exposures simultaneously. Objectives: This study aims to evaluate the association between a wide range of prenatal and postnatal exposures and blood pressure (BP) in children. Methods: Systolic and diastolic BP were measured among 1,277 children from the European HELIX (Human Early-Life Exposome) cohort aged 6 to 11 years. Prenatal (n = 89) and postnatal (n = 128) exposures include air pollution, built environment, meteorology, natural spaces, traffic, noise, chemicals, and lifestyles. Two methods adjusted for confounders were applied: an exposome-wide association study considering the exposures independently, and the deletion-substitution-addition algorithm considering all the exposures simultaneously. Results: Decreases in systolic BP were observed with facility density (β change for an interquartile-range increase in exposure: −1.7 mm Hg [95% confidence interval (CI): −2.5 to −0.8 mm Hg]), maternal concentrations of polychlorinated biphenyl 118 (−1.4 mm Hg [95% CI: −2.6 to −0.2 mm Hg]) and child concentrations of dichlorodiphenyldichloroethylene (DDE: −1.6 mm Hg [95% CI: −2.4 to −0.7 mm Hg]), hexachlorobenzene (−1.5 mm Hg [95% CI: −2.4 to −0.6 mm Hg]), and mono−benzyl phthalate (−0.7 mm Hg [95% CI: −1.3 to −0.1 mm Hg]), whereas increases in systolic BP were observed with outdoor temperature during pregnancy (1.6 mm Hg [95% CI: 0.2 to 2.9 mm Hg]), high fish intake during pregnancy (2.0 mm Hg [95% CI: 0.4 to 3.5 mm Hg]), maternal cotinine concentrations (1.2 mm Hg [95% CI: -0.3 to 2.8 mm Hg]), and child perfluorooctanoate concentrations (0.9 mm Hg [95% CI: 0.1 to 1.6 mm Hg]). Decreases in diastolic BP were observed with outdoor temperature at examination (−1.4 mm Hg [95% CI: −2.3 to −0.5 mm Hg]) and child DDE concentrations (−1.1 mm Hg [95% CI: −1.9 to −0.3 mm Hg]), whereas increases in diastolic BP were observed with maternal bisphenol-A concentrations (0.7 mm Hg [95% CI: 0.1 to 1.4 mm Hg]), high fish intake during pregnancy (1.2 mm Hg [95% CI: −0.2 to 2.7 mm Hg]), and child copper concentrations (0.9 mm Hg [95% CI: 0.3 to 1.6 mm Hg]). Conclusions: This study suggests that early-life exposure to several chemicals, as well as built environment and meteorological factors, may affect BP in children

Evaluation of land use regression models for NO2 and particulate matter in 20 European study areas: The ESCAPE project

Land use regression models (LUR) frequently use leave-one-out-cross- validation (LOOCV) to assess model fit, but recent studies suggested that this may overestimate predictive ability in independent data sets. Our aim was to evaluate LUR models for nitrogen dioxide (NO2) and particulate matter (PM) components exploiting the high correlation between concentrations of PM metrics and NO2. LUR models have been developed for NO2, PM2.5 absorbance, and copper (Cu) in PM10 based on 20 sites in each of the 20 study areas of the ESCAPE project. Models were evaluated with LOOCV and "hold-out evaluation (HEV)" using the correlation of predicted NO2 or PM concentrations with measured NO2 concentrations at the 20 additional NO2 sites in each area. For NO2, PM2.5 absorbance and PM10 Cu, the median LOOCV R2s were 0.83, 0.81, and 0.76 whereas the median HEV R 2 were 0.52, 0.44, and 0.40. There was a positive association between the LOOCV R2 and HEV R2 for PM2.5 absorbance and PM10 Cu. Our results confirm that the predictive ability of LUR models based on relatively small training sets is overestimated by the LOOCV R2s. Nevertheless, in most areas LUR models still explained a substantial fraction of the variation of concentrations measured at independent sites. © 2013 American Chemical Society

Early-life environmental exposures and childhood obesity: an exposome-wide approach

Background: Chemical and nonchemical environmental exposures are increasingly suspected to influence the development of obesity, especially during early life, but studies mostly consider single exposure groups. Objectives: Our study aimed to systematically assess the association between a wide array of early-life environmental exposures and childhood obesity, using an exposome-wide approach. Methods: The HELIX (Human Early Life Exposome) study measured child body mass index (BMI), waist circumference, skinfold thickness, and body fat mass in 1,301 children from six European birth cohorts age 6-11 y. We estimated 77 prenatal exposures and 96 childhood exposures (cross-sectionally), including indoor and outdoor air pollutants, built environment, green spaces, tobacco smoking, and biomarkers of chemical pollutants (persistent organic pollutants, metals, phthalates, phenols, and pesticides). We used an exposure-wide association study (ExWAS) to screen all exposure-outcome associations independently and used the deletion-substitution-addition (DSA) variable selection algorithm to build a final multiexposure model. Results: The prevalence of overweight and obesity combined was 28.8%. Maternal smoking was the only prenatal exposure variable associated with higher child BMI (z-score increase of 0.28, 95% confidence interval: 0.09, 0.48, for active vs. no smoking). For childhood exposures, the multiexposure model identified particulate and nitrogen dioxide air pollution inside the home, urine cotinine levels indicative of secondhand smoke exposure, and residence in more densely populated areas and in areas with fewer facilities to be associated with increased child BMI. Child blood levels of copper and cesium were associated with higher BMI, and levels of organochlorine pollutants, cobalt, and molybdenum were associated with lower BMI. Similar results were found for the other adiposity outcomes. Discussion: This first comprehensive and systematic analysis of many suspected environmental obesogens strengthens evidence for an association of smoking, air pollution exposure, and characteristics of the built environment with childhood obesity risk. Cross-sectional biomarker results may suffer from reverse causality bias, whereby obesity status influenced the biomarker concentration. https://doi.org/10.1289/EHP5975.This study received funding from the European Community’s Seventh Framework Programme (FP7/2007-2013) under grant agreement no. 308333 – the HELIX project for data collection and analyses. The HELIX program built on six existing cohorts that received previous funding, including the major ones listed below. INMA data collections were supported by grants from the Instituto de Salud Carlos III, CIBERESP, and the Generalitat de Catalunya-CIRIT. KANC was funded by the grant of the Lithuanian Agency for Science Innovation and Technology (6-04-2014_31V-66). The Norwegian Mother and Child Cohort Study (MoBa) is supported by the Norwegian Ministry of Health and the Ministry of Education and Research, National Institutes of Health (NIH)/ National Institute of Environmental Health Sciences (NIEHS) (contract no. N01-ES-75558), and NIH/NINDS (grants 1 UO1 NS 047537-01 and 2 UO1 NS 047537-06A1). The Rhea project was financially supported by European projects and the Greek Ministry of Health (Program of Prevention of Obesity and Neurodevelopmental Disorders in Preschool Children in Heraklion district, Crete, Greece: 2011–2014; “Rhea Plus,” Primary Prevention Program of Environmental Risk Factors for Reproductive Health, and Child Health: 2012–2015). M.C. received funding from Instituto de Salud Carlos III (Ministry of Economy and Competitiveness) (MS16/00128). L.C. was supported by the NIH/NIEHS grants R21ES029681, R01ES030691, R01ES029944, R01 ES030364, R21ES028903, and P30ES007048

Development of land use regression models for PM2.5, PM 2.5 absorbance, PM10 and PMcoarse in 20 European study areas; Results of the ESCAPE project

Land Use Regression (LUR) models have been used increasingly for modeling small-scale spatial variation in air pollution concentrations and estimating individual exposure for participants of cohort studies. Within the ESCAPE project, concentrations of PM2.5, PM2.5 absorbance, PM10, and PMcoarse were measured in 20 European study areas at 20 sites per area. GIS-derived predictor variables (e.g., traffic intensity, population, and land-use) were evaluated to model spatial variation of annual average concentrations for each study area. The median model explained variance (R2) was 71% for PM2.5 (range across study areas 35-94%). Model R2 was higher for PM2.5 absorbance (median 89%, range 56-97%) and lower for PMcoarse (median 68%, range 32- 81%). Models included between two and five predictor variables, with various traffic indicators as the most common predictors. Lower R2 was related to small concentration variability or limited availability of predictor variables, especially traffic intensity. Cross validation R2 results were on average 8-11% lower than model R2. Careful selection of monitoring sites, examination of influential observations and skewed variable distributions were essential for developing stable LUR models. The final LUR models are used to estimate air pollution concentrations at the home addresses of participants in the health studies involved in ESCAPE. © 2012 American Chemical Society